• Exam for exchange students: 21.12.2023 at 16:15 in room 01-013.
• Materials on text analysis and image analysis (lecture 6) is for self study
• The mock exam is online 💣
  • Central exam from last year
  • Solutions won’t be discussed in the lecture: use the forum on Canvas
  • No forum supervision/email during the learning phase guaranteed (from me or the TAs)
• Walk-ins for the digital exam: see announcement in Canvas

Rectangular data

• Import data from text files, csv, tsv, etc.
• Tibbles, data frames in R

Non-rectangular data

• Hierarchical data (xml, html, json)
• Unstructured text data
• Images/Pictures data

Tell your future self what this script is all about

#######################################################################
# Project XY: Data Gathering and Import
#
# This script is the first part of the data pipeline of project XY.
# It imports data from ...
# Input: links to data sources (data comes in ... format)
# Output: cleaned data as CSV
# 
# A. Sallin, St. Gallen, 2023
#######################################################################


# SET UP --------------
# load packages
library(tidyverse)

# set fix variables
INPUT_PATH <- "/rawdata"
OUTPUT_FILE <- "/final_data/datafile.csv"


# IMPORT RAW DATA FROM CSVs -------------

Be the JSON file

{
  "students": [
    {
      "id": 19091,
      "firstName": "Peter",
      "lastName": "Mueller",
      "grades": {
          "micro": 5,
          "macro": 4.5,
          "data handling": 5.5
          }
    },
    {
      "id": 19092,
      "firstName": "Anna",
      "lastName": "Schmid",
      "grades": {
          "micro": 5.25,
          "macro": 4,
          "data handling": 5.75
          }
    },
    {
      "id": 19093,
      "firstName": "Noah",
      "lastName": "Trevor",
      "grades": {
          "micro": 4,
          "macro": 4.5,
          "data handling": 5
          }
    }
  ]
}

Write an R code to extract a table with, as a first column, a vector of first names, and as a second column, the average grade per student. The table can be a data frame or a tibble.

<students>
  <student>
    <id>19091</id>
    <firstName>Peter</firstName>
    <lastName>Mueller</lastName>
    <grades>
      <micro>5</micro>
      <macro>4.5</macro>
      <dataHandling>5.5</dataHandling>
    </grades>
  </student>
  <student>
    <id>19092</id>
    <firstName>Anna</firstName>
    <lastName>Schmid</lastName>
    <grades>
      <micro>5.25</micro>
      <macro>4</macro>
      <dataHandling>5.75</dataHandling>
    </grades>
  </student>
  <student>
    <id>19093</id>
    <firstName>Noah</firstName>
    <lastName>Trevor</lastName>
    <grades>
      <micro>4</micro>
      <macro>4.5</macro>
      <dataHandling>5</dataHandling>
    </grades>
  </student>
</students>

• ‘students’ is the root-node, ‘grades’ are its children
• The siblings of Trevor Noah are Anna Schmid and Peter Mueller
• The code below would be an alternative, equivalent notation for the third student in the xml file above.

<student id="19093" firstName="Noah" lastName="Trevor">
      <grades micro="4" macro="4.5" dataHandling="5" />
</student>

• Recognize where the problems are in a given dataset, and what is in the way of a proper analysis of the data.
• Organize your work: what needs to be addressed first?

• Use simple string-operations to clean text variables.
• Reshape datasets from wide to long (and vice versa).
• Apply row-binding/stacking of datasets.

• In practice: still a long way to go.
• Parsable, but messy data: inconsistencies, data types, missing observations, wide format.

• In practice: still a long way to go.
• Parsable, but messy data: Inconsistencies, data types, missing observations, wide format.

• Goal of data preparation: dataset is ready for analysis.
• Key conditions:
  1. Data values are consistent/clean within each variable.
  2. Variables are of proper data types.
  3. Dataset is in ‘tidy’ (long) format.

• Tidy data
• Reshaping
• Stacking

Following Wickham (2014), a tidy dataset is tidy when…

1. Each variable is a column; each column is a variable.
2. Each observation is a row; each row is an observation.
3. Each value is a cell; each cell is a single value.

Messy:

##       measure Jan.1 Jan.2 Jan.3
## 1 Temperature    20    22    21
## 2    Humidity    80    78    82

Tidy:

…

Messy 💩

##       measure Jan.1 Jan.2 Jan.3
## 1 Temperature    20    22    21
## 2    Humidity    80    78    82

Tidy 😎

## # A tibble: 3 × 3
##   Date  Temperature Humidity
##   <chr>       <dbl>    <dbl>
## 1 Jan.1          20       80
## 2 Jan.2          22       78
## 3 Jan.3          21       82

Messy:

##   year temperature_location
## 1 2019           22C_London
## 2 2019            18C_Paris
## 3 2019             25C_Rome

Tidy:

homework..

Messy:

##    Student Econ DataHandling Management
## 1 Johannes 5.00          4.0        5.5
## 2   Hannah 5.25          4.5        6.0
## 3     Igor 4.00          5.0        6.0

Tidy:

homework..

• From wide to long: melt(), gather(),

👉 We’ll use tidyverse::pivot_longer().

• From long to wide: cast(), spread(),

👉 We’ll use tidyverse::pivot_wider().

• Use rbind() in base R
  • Requires that the data frames have the same column names and same column classes.

• Use bind_rows() from dplyr()
  • More flexible
  • Binds data frames with different column names and classes
  • Automatically fills missing columns with NA

For these reasons (+ performance, handling or row names, and handling of factors), dplyr::bind_rows() is preferred in most applications.